T demonstrate post hoc robustness of decision problems to parameter estimates, analysts may conduct a probabilistic sensitivity analysis, assigning distributions to uncertain parameters and computing the probability of decision change. In contrast to classical threshold proximity methods of sensitivity analysis, no appealing graphical methods are available to present the results of a probabilis...

In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions for probabilities, rates, and relative effect parameters, for both controlled and...

Health economic evaluations have recently become an important part of the clinical and medical research process and have built upon more advanced statistical decision-theoretic foundations. In some contexts, it is officially required that uncertainty about both parameters and observable variables be properly taken into account, increasingly often by means of Bayesian methods. Among these, proba...

Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of biochemical reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from seve...

The uncertainty of renewable energy sources (RESs) presents a challenge for power grid operation. To analyze the variability RESs, probabilistic flow (PPF) method has been introduced. This can be used to evaluate RES generation and its effects on system voltage flow. In this study, an analytical approach with PPF calculate line sensitivity by is proposed, which directly probability distribution...

of the Dissertation Sensitivity Analysis of Probabilistic Graphical Models

OBJECTIVE To give guidance in defining probability distributions for model inputs in probabilistic sensitivity analysis (PSA) from a full Bayesian perspective. METHODS A common approach to defining probability distributions for model inputs in PSA on the basis of input-related data is to use the likelihood of the data on an appropriate scale as the foundation for the distribution around the i...